The most important public sources of genetic data for understanding human population genetics to date have come from the
HapMap and the
Human Genome Diversity Panel. A
new paper presents an analysis of human population structure in a somewhat complementary data set assembled from thousands of samples largely from Mexico, Europe, East Asia, and Central Asia (the European population in this data were previously
examined in great detail). A couple highlights:
1. I recently
mentioned a pair of papers that presented conflicting results about the relative effective population sizes of the X chromosome and the autosomes. In this paper, the authors write:
Interestingly, we observed a significantly higher degree of divergence in allele frequency across X chromosome SNPs where we estimate FST to be 9.7%. This value is about 40% higher than the expected value of 6.8% derived from a many-deme island model and accounting for the 4:3 ratio of autosomes to sex chromosome. The higher degree of population divergence at X chromosome SNPs suggests a smaller effective population size of the X than that predicted from Mendelian genetics.
This is additional evidence that the observation that needs to be explained is a lower Ne on the X chromosomes as compared to the autosomes, rather than the reverse.
2. Within Europe, the authors find that, in general, haplotype diversity decreases from the south to the north, an observation consistent with expansion from the Middle East into Europe via a series of
serial bottlenecks. However, there is high haplotype diversity in Southwestern Europe, which is inconsistent with such a simple model. The authors show that many of the SW European haplotypes match up with those in Africa, suggesting recent migration directly from Africa across the Mediterranean could partially explain this phenomenon.
I may have more to say once the Supplementary Information are available online, but this is a nice example of leveraging samples collected for medical genetics studies around the world for further understanding in population genetics.
Labels: Population genetics
